in a normal model fit on a dataset that looks like this :
> head(total)
# A tsibble: 6 x 15 [1D]
# Key: id [6]
Date Close Interest_Rate Consumer_Inflation `CPI(YOY)` `Wage_Index(QoQ)` `Wage_Index(YoY)` AiG_idx TD_Inflation CFTC_AUD_net_positions `RBA_Mean_CPI(Yo~ Commonwealth_Ba~
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2009-04-01 69.4 0 0 0 0 0 0 0 0 0 0
2 2009-04-02 71.6 0 0 0 0 0 0 0 0 0 0
3 2009-04-03 71.0 0 0 0 0 0 0 0 0 0 0
4 2009-04-06 71.0 0 0 0 0 0 0 0 0 0 0
5 2009-04-07 71.6 3 0 0 0 0 0 0 0 0 0
6 2009-04-08 71.1 3 0 0 0 0 0 0 0 0 0
the training model :
fit <- total_axy %>%
model(
fable::TSLM(Close)
)
report(axy_fit)
is training
1-10 of 3,409 rows | 1-10 of 16 columns
1 model per row! how can I solve this ? I just want 1 model for all rows!!!
You get one model per row because your key is set to id, which I guess is set to a unique value per row. You can see that there are 6 rows and 6 unique values of id.
According to the package website a tsibble has a key attribute which should be:
a set of variables that define observational units over time
To fix it, try changing the key or removing it.
library(dplyr)
library(tsibble)
library(fpp3)
total <-
tibble(
Date = seq.Date(from = as.Date("2009-04-01"), to = as.Date("2009-04-08"), by = 1),
Close = rnorm(length(Date))
) %>%
# Make a tsibble with no key
as_tsibble(index = Date)
fit <-
total %>%
model(
fable::TSLM(Close)
)
report(fit)
This gives only one model.